First, access your Google Search Console account. Navigate to the property you want to export data from. Use the "Performance" report to view your data. You can export the desired data by clicking on the "Export" button, which allows you to download the data in CSV format. Save this file on your local machine for further processing.

Ensure you have the necessary tools to process and transform the CSV data. Install Python and any required libraries such as `pandas` and `teradatasql` to facilitate data manipulation and connection to Teradata. This setup will allow you to read and prepare your data for import into Teradata.

Use Python to read the CSV file using `pandas`. Transform the data as needed to match the schema of your Teradata Vantage database. This may include renaming columns, changing data types, or filtering specific data. Save the transformed data into a new CSV file or a `DataFrame` in memory.

```python
import pandas as pd

df = pd.read_csv('your_exported_file.csv')
# Perform transformations
transformed_df = df.rename(columns={'old_name': 'new_name'})
```

Ensure you have access credentials (username, password) and connection details (host, database name) for your Teradata Vantage instance. Verify that your user account has the necessary permissions to create tables and insert data into the database.

Use Teradata SQL Assistant or another SQL client to connect to Teradata Vantage. Write and execute SQL statements to create the necessary tables that match the structure of your transformed data. This step ensures that the data can be inserted without errors.

```sql
CREATE TABLE your_table (
column1 VARCHAR(255),
column2 INT,
...
);
```

Use Python and the `teradatasql` library to connect to your Teradata Vantage instance. Load the transformed data directly into Teradata using SQL `INSERT` statements or the `BULK INSERT` command for efficiency. This is done within a Python script using a connection to the database.

```python
import teradatasql

# Establish connection
with teradatasql.connect(host='your_host', user='your_user', password='your_password') as con:
cursor = con.cursor()
for index, row in transformed_df.iterrows():
cursor.execute("INSERT INTO your_table (column1, column2) VALUES (?, ?)", (row['column1'], row['column2']))
```

After loading, verify that the data in Teradata matches the expected results. Run queries to check the row count and specific data points against your original CSV file. This ensures the data was accurately transferred and transformed.

```sql
SELECT COUNT() FROM your_table;
SELECT FROM your_table WHERE condition_to_verify;
```

By following these steps, you can manually move data from Google Search Console to Teradata Vantage without relying on third-party connectors or integrations.